Quantum Accurate Machine Learning Interatomic Potential for Large-scale Simulations of Deuterium Under Shock

Large-scale molecular dynamics (MD) simulations of inertial confinement fusion (ICF) experiments naturally include atomistic level microscopic physics missing from traditional radiation-hydrodynamic codes, and thus can model kinetic processes such as species separation in CH plastic ablators and the subsequent hydrogen streaming and mixing into the deuterium-tritium (DT) fuel.

To directly simulate a shock transiting through the ablator-fuel interface, we require interatomic potentials that accurately describe the interactions between the different species from ambient to the eV temperatures and multi-megabar pressures encountered in experiments. We will present work on developing a broadly transferrable quantum-accurate machine learning potential for deuterium gas using the Chebyshev Interaction Model for Efficient Simulations (ChIMES) framework. We show that this model is able to reproduce the equation of state, radial distribution functions, and Hugoniot relations predicted from density functional theory for densities up to ~0.8 g/cm^3 and temperatures up to ~60,000 K.

We will also discuss ideas for improving the potential model via the inclusion of a temperature-dependent force correction term to include the effects of ionization at high temperatures. These ideas will be employed in a future work to generate potentials for CH-DT compounds to allow for simulations of the ablator-fuel interface with ab initio levels of accuracy.